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This article mainly introduces what apache hadoop refers to, the article is very detailed, has a certain reference value, interested friends must read it!

Apache Hadoop is a framework for running applications on large clusters built with general hardware. It implements the Map/Reduce programming paradigm, where computing tasks are divided into small blocks (multiple times) to run on different nodes.

In addition, it provides a distributed file system (HDFS) where data is stored on computing nodes to provide extremely high aggregate bandwidth across data centers.

Frame function

The New Choice of the ownership of Apache Hadoop big data

Physical DAS is still the best storage medium for Apache Hadoop, because the relevant high-level professional and business companies determine the storage media through research and practice. However, there is a big problem with Apache Hadoop data storage based on HDFS.

First, the default solution is for all Apache Hadoop data to be copied, moved, and then backed up. HDFS is based on the optimization of Apache Hadoop big data block, which saves the time of Apache Hadoop data exchange. Future use usually means that Apache Hadoop data is copied out. Although there are local snapshots, they are not exactly consistent or not fully recoverable at a point in time.

For these and other reasons, enterprise storage vendors are smart to change HDFS, and some technocratic big data experts make Apache Hadoop computing take advantage of external storage. But for many enterprises, Apache Hadoop provides a good compromise: there is no need for high maintenance storage or storage adaptation to new maintenance methods, but there is a certain cost.

Many Apache Hadoop vendors, which provide interfaces to the remote HDFS of Apache Hadoop clusters, are the first choice for Apache Hadoop enterprises with large business volumes. Because they will be in isilon, carry out any other Apache Hadoop data processing big data's protection, including Apache Hadoop security and other issues. Another benefit is that data stored externally can usually access the storage of other Apache Hadoop protocols, support workflows and restrict the transfer of data and copies of data needed within the enterprise. Apache Hadoop is also based on this principle to handle big data, a large data reference architecture, combined with a combined storage solution, directly into the Apache Hadoop cluster.

It is also worth mentioning that the virtualized Apache Hadoop big data analysis. In theory, all compute and storage nodes can be virtualized. VMware and RedHat/OpenStack have Hadoop's virtualization solution. However, almost all Apache Hadoop host nodes cannot solve the storage problem of the enterprise. It simulates the Apache Hadoop computing aspect to enable enterprises to accelerate and dump the existing dataset, SAN/NAS--, under the coverage of its Apache Hadoop HDFS. In this way, Apache Hadoop big data Analytics can make no changes to the data in a data center, thus using the new Apache Hadoop storage architecture and new data streams or data management changes.

Most Apache Hadoop distributions start with open source HDFS near Apache Hadoop (currently software-defined storage big data). The difference is that Apache Hadoop takes a different approach. This is basically the storage required by the enterprise Apache Hadoop to build its own compatible storage layer on the Apache Hadoop HDFS. The MAPR version is fully capable of handling Imax O snapshot replication support, and Apache Hadoop is also compatible with other natively supported protocols, such as NFS. Apache Hadoop is also very effective and helps to provide mainly enterprise business intelligence applications, and running decision support solutions depends on big data's history and real-time information. Similarly, IBM has launched a high-performance computing system that stores API as an Apache Hadoop distribution as an alternative to HDFS

Another interesting solution for Apache Hadoop can help solve data problems. One is dataguise, where data security starts, and some unique IP,Apache Hadoop that can effectively protect large data sets of Apache Hadoop can automatically identify and globally cover or encrypt sensitive data in a large data cluster. Horizontal data science is an emerging technology in this field. If you connect to your data file and log in to Apache Hadoop, even HDFS,Apache Hadoop will automatically store the data no matter where it is. The products provided by Apache Hadoop big data help to quickly set up business applications and use the source and location of the data to count the information needed by the business.

If you have always been interested in Apache Hadoop management or enterprise data center storage, this is a good time to update your own knowledge of Apache Hadoop big data. If you want to keep up with Apache Hadoop big data, you should not reject the application of Apache Hadoop new technologies.

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